Concentration of Infectious Aquatic Rhabdoviruses from Freshwater and Seawater Using Ultrafiltration
Bibliographic record
Abstract
Infectious hematopoietic necrosis virus (IHNV), viral hemorrhagic septicemia virus, and spring viremia of carp virus were concentrated and detected from freshwater and seawater samples by using hollow-fiber ultrafiltration. Within 60 min, virus in a 50-L freshwater or saltwater sample was concentrated more than 70-fold, and virus retention efficiencies were consistently greater than 88%. Retention efficiency was highly dependent upon concentrations of column blocking and sample stabilization solutions. A large column with a surface area of 1.15 m2 and a filtration capacity of 5-200 L exhibited optimal viral retention when blocked with 2% fetal bovine serum (FBS) and when the samples were supplemented with 0.1% FBS. Conversely, a small column with 100-fold less surface area and a filtering capacity of 0.5-2.0 L was optimized when blocked with 1% FBS and when the samples were supplemented with 0.1% FBS. The optimized ultrafiltration procedure was further validated with water from a tank that contained IHNV-exposed juvenile sockeye salmon Oncorhynchus nerka, resulting in an average virus retention efficiency of 91.6 +/- 4.1% (mean +/- SE). Virus quantification of concentrated samples demonstrated that IHNV shedding in sockeye salmon preceded mortality; shedding of the virus was observed to increase significantly as early as 7 d postchallenge and peaked at day 14, when virus levels reached 4.87 x 10(3) plaque-forming units/mL. We conclude that ultrafiltration is a reliable and effective method for concentrating viable aquatic rhabdoviruses from large volumes of water and has application for the analysis of environmental water samples.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".